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Mixed T-domain and TF-domain Magnitude and Phase representations for GAN-based speech enhancement
- Source :
- Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
- Publication Year :
- 2024
- Publisher :
- Nature Portfolio, 2024.
-
Abstract
- Abstract Deep learning has made significant advancements in speech enhancement, which plays a crucial role in improving the quality of speech signals in noisy conditions. In this paper, we propose a new approach called M-DGAN, which introduces a time (T)-domain encoder-decoder structure with rich channel representations into the time-frequency (TF)-domain generator framework, resulting in a new generator structure with mixed magnitude and phase representations in the T and TF-domains. The proposed mixed T-domain and TF-domain generator, incorporating the cascaded reworked conformer (CRC) structure, exhibits improved modeling capability and adaptability. Test results on the Voice Bank + DEMAND public dataset show that our method achieves the highest score with $$PSEQ=3.52$$ P S E Q = 3.52 and performs well on all the remaining metrics when compared to the current state-of-the-art methods. In addition, tests on the NISQA_TEST_LIVETALK real dataset of the NISQA Corpus show the breadth and robustness of our model on speech enhancement tasks.
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 14
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f06d33433744445b90cead05034aac6
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41598-024-68708-w